from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	2.153970	0.152208	NaN	0.000371	0.002154	brute	-1	1	0.663	0.220784	0.005899	0.687	9.756003	9.759485
4	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	3.287839	0.092061	NaN	0.000243	0.003288	brute	-1	5	0.757	0.214187	0.001486	0.742	15.350334	15.350704
7	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.300400	0.013344	NaN	0.000348	0.002300	brute	1	100	0.882	0.264998	0.003964	0.875	8.680811	8.681782
8	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.023044	0.000150	NaN	0.000035	0.023044	brute	1	100	1.000	0.008720	0.000197	0.000	2.642717	2.643391
10	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	3.070885	0.033928	NaN	0.000261	0.003071	brute	-1	100	0.882	0.262483	0.002708	0.875	11.699366	11.699989
11	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.026615	0.002553	NaN	0.000030	0.026615	brute	-1	100	1.000	0.009241	0.002017	0.000	2.880136	2.947936
13	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.310144	0.006965	NaN	0.000346	0.002310	brute	1	5	0.757	0.210848	0.002855	0.742	10.956463	10.957467
16	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.252624	0.002810	NaN	0.000639	0.001253	brute	1	1	0.663	0.207930	0.002069	0.687	6.024242	6.024541
19	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.795357	0.028980	NaN	0.000009	0.001795	brute	-1	1	0.896	0.032455	0.000429	0.967	55.317854	55.322675
22	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.909159	0.043217	NaN	0.000005	0.002909	brute	-1	5	0.922	0.034749	0.001929	0.974	83.718749	83.847708
25	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.185421	0.003420	NaN	0.000007	0.002185	brute	1	100	0.929	0.076490	0.002192	0.975	28.571147	28.582879
28	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	3.161772	0.093585	NaN	0.000005	0.003162	brute	-1	100	0.929	0.075084	0.000664	0.975	42.110040	42.111689
31	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.193757	0.010071	NaN	0.000007	0.002194	brute	1	5	0.922	0.034004	0.000629	0.974	64.513876	64.524904
34	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.103411	0.006045	NaN	0.000015	0.001103	brute	1	1	0.896	0.032155	0.000295	0.967	34.315759	34.317199

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.345	0.0	-1	1	0.051	0.005	0.246	0.247	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.0	5.906	0.0	-1	5	0.049	0.000	0.275	0.275	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.011	0.0	1	100	0.049	0.000	0.274	0.274	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.296	0.0	-1	100	0.049	0.000	0.260	0.260	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.012	0.0	6.474	0.0	1	5	0.048	0.000	0.255	0.255	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.013	0.0	6.023	0.0	1	1	0.048	0.000	0.274	0.274	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.302	0.0	-1	1	0.010	0.000	0.549	0.549	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.320	0.0	-1	5	0.010	0.000	0.515	0.515	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.303	0.0	1	100	0.010	0.000	0.542	0.542	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.312	0.0	-1	100	0.010	0.000	0.526	0.526	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.303	0.0	1	5	0.010	0.000	0.539	0.540	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.0	0.302	0.0	1	1	0.010	0.000	0.543	0.543	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.154	0.152	0.000	0.002	-1	1	0.221	0.006	9.756	9.759	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.002	0.000	0.027	-1	1	0.009	0.000	3.049	3.050	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.288	0.092	0.000	0.003	-1	5	0.214	0.001	15.350	15.351	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.028	0.004	0.000	0.028	-1	5	0.008	0.000	3.326	3.327	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.300	0.013	0.000	0.002	1	100	0.265	0.004	8.681	8.682	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.000	0.000	0.023	1	100	0.009	0.000	2.643	2.643	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	3.071	0.034	0.000	0.003	-1	100	0.262	0.003	11.699	11.700	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.027	0.003	0.000	0.027	-1	100	0.009	0.002	2.880	2.948	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.310	0.007	0.000	0.002	1	5	0.211	0.003	10.956	10.957	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.024	0.000	0.000	0.024	1	5	0.008	0.000	2.879	2.882	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.253	0.003	0.001	0.001	1	1	0.208	0.002	6.024	6.025	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	1	1	0.008	0.000	2.803	2.804	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.795	0.029	0.000	0.002	-1	1	0.032	0.000	55.318	55.323	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.000	0.006	-1	1	0.001	0.000	8.193	8.221	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.909	0.043	0.000	0.003	-1	5	0.035	0.002	83.719	83.848	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.013	0.004	0.000	0.013	-1	5	0.001	0.000	17.248	17.295	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.185	0.003	0.000	0.002	1	100	0.076	0.002	28.571	28.583	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	100	0.001	0.000	4.298	4.305	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	3.162	0.094	0.000	0.003	-1	100	0.075	0.001	42.110	42.112	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.005	0.000	0.010	-1	100	0.001	0.000	10.183	10.290	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.194	0.010	0.000	0.002	1	5	0.034	0.001	64.514	64.525	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.004	0.000	0.000	0.004	1	5	0.001	0.000	4.754	4.767	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.103	0.006	0.000	0.001	1	1	0.032	0.000	34.316	34.317	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.670	2.676	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.862342	1.047025	NaN	0.000093	0.000862	kd_tree	-1	1	0.929	0.114904	0.002498	0.910	7.504871	7.506645
4	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.141153	0.413375	NaN	0.000070	0.001141	kd_tree	-1	5	0.946	0.206937	0.002263	0.941	5.514502	5.514831
7	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	5.865079	0.462938	NaN	0.000014	0.005865	kd_tree	1	100	0.951	0.629273	0.012421	0.940	9.320398	9.322213
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	3.428704	0.220996	NaN	0.000023	0.003429	kd_tree	-1	100	0.951	0.619292	0.006916	0.940	5.536494	5.536840
13	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.768106	0.119270	NaN	0.000045	0.001768	kd_tree	1	5	0.946	0.210532	0.004980	0.941	8.398288	8.400638
16	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.895234	0.172745	NaN	0.000089	0.000895	kd_tree	1	1	0.929	0.116658	0.013346	0.910	7.673978	7.724035
19	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.032551	0.018237	NaN	0.000492	0.000033	kd_tree	-1	1	0.891	0.000484	0.000080	0.879	67.246767	68.153671
22	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.027045	0.001408	NaN	0.000592	0.000027	kd_tree	-1	5	0.911	0.000757	0.000033	0.905	35.735502	35.768460
25	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.040791	0.007561	NaN	0.000392	0.000041	kd_tree	1	100	0.894	0.005784	0.000939	0.917	7.052774	7.145113
28	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.044051	0.005763	NaN	0.000363	0.000044	kd_tree	-1	100	0.894	0.006336	0.001898	0.917	6.952703	7.258124
31	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.023866	0.000403	NaN	0.000670	0.000024	kd_tree	1	5	0.911	0.000758	0.000033	0.905	31.485393	31.514429
34	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.021472	0.000124	NaN	0.000745	0.000021	kd_tree	1	1	0.891	0.000450	0.000037	0.879	47.742564	47.906895

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.210	0.015	0.025	0.0	-1	1	0.845	0.035	3.797	3.801	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.739	0.165	0.017	0.0	-1	5	0.828	0.007	5.723	5.723	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.349	0.040	0.018	0.0	1	100	0.797	0.009	5.457	5.457	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.346	0.032	0.018	0.0	-1	100	0.830	0.007	5.235	5.235	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.612	0.078	0.017	0.0	1	5	0.797	0.010	5.785	5.786	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.611	0.090	0.017	0.0	1	1	0.831	0.017	5.546	5.547	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.018	0.0	-1	1	0.004	0.002	0.250	0.291	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.025	0.0	-1	5	0.001	0.001	0.522	0.580	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	100	0.001	0.001	0.480	0.535	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	-1	100	0.001	0.000	0.475	0.495	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	5	0.001	0.000	0.574	0.574	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.026	0.0	1	1	0.001	0.000	0.594	0.595	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.862	1.047	0.000	0.001	-1	1	0.115	0.002	7.505	7.507	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	11.125	11.527	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.141	0.413	0.000	0.001	-1	5	0.207	0.002	5.515	5.515	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.000	0.000	0.004	-1	5	0.000	0.000	7.948	8.292	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.865	0.463	0.000	0.006	1	100	0.629	0.012	9.320	9.322	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	4.439	4.564	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.429	0.221	0.000	0.003	-1	100	0.619	0.007	5.536	5.537	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.007	0.001	0.000	0.007	-1	100	0.001	0.000	7.821	8.074	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.768	0.119	0.000	0.002	1	5	0.211	0.005	8.398	8.401	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.001	0.000	3.686	3.784	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.895	0.173	0.000	0.001	1	1	0.117	0.013	7.674	7.724	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.180	3.291	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.033	0.018	0.000	0.000	-1	1	0.000	0.000	67.247	68.154	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	35.614	37.240	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.001	0.001	0.000	-1	5	0.001	0.000	35.736	35.768	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.703	23.676	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.041	0.008	0.000	0.000	1	100	0.006	0.001	7.053	7.145	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	4.630	4.988	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.044	0.006	0.000	0.000	-1	100	0.006	0.002	6.953	7.258	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.003	0.002	0.000	0.003	-1	100	0.000	0.000	32.426	34.088	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.000	0.001	0.000	1	5	0.001	0.000	31.485	31.514	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.982	7.324	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.021	0.000	0.001	0.000	1	1	0.000	0.000	47.743	47.907	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.726	7.037	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.603	0.081	30	0.027	0.0	random	0.504	0.043	1.195	1.200	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.665	0.017	30	0.024	0.0	k-means++	0.528	0.032	1.259	1.261	See	See
6	KMeans_tall	fit	1000000	1000000	100	6.605	0.222	30	0.121	0.0	random	3.074	0.043	2.148	2.149	See	See
9	KMeans_tall	fit	1000000	1000000	100	6.817	0.052	30	0.117	0.0	k-means++	3.219	0.051	2.118	2.118	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.007	0.000	random	0.0	0.0	9.970	13.715	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	9.167	14.054	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.010	0.000	k-means++	0.0	0.0	9.435	10.328	See	See
5	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	13.086	14.272	See	See
7	KMeans_tall	predict	1000000	1000	100	0.003	0.001	30	0.317	0.000	random	0.0	0.0	8.921	9.437	See	See
8	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.001	0.002	random	0.0	0.0	11.591	11.872	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.395	0.000	k-means++	0.0	0.0	6.171	6.367	See	See
11	KMeans_tall	predict	1000000	1	100	0.002	0.000	30	0.000	0.002	k-means++	0.0	0.0	12.328	12.491	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 4 predictions (50.0%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
1	KMeans_short	predict	0.001090	10000	1000	2	0.002274	0.000144	20	0.007036	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000559	0.000042	-0.000965	4.066435	4.078042
4	KMeans_short	predict	0.001995	10000	1000	2	0.002095	0.000093	20	0.007636	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000579	0.000034	-0.000750	3.617104	3.623289
7	KMeans_short	predict	0.015034	10000	1000	100	0.002918	0.000122	20	0.274130	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001217	0.000117	0.293767	2.398745	2.409838
10	KMeans_short	predict	0.060044	10000	1000	100	0.003082	0.000184	20	0.259563	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001163	0.000085	0.256968	2.650903	2.657993

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.096	0.001	20	0.002	0.0	random	0.034	0.002	2.859	2.865	See	See
3	KMeans_short	fit	10000	10000	2	0.268	0.003	20	0.001	0.0	k-means++	0.099	0.000	2.712	2.712	See	See
6	KMeans_short	fit	10000	10000	100	0.247	0.005	20	0.032	0.0	random	0.136	0.001	1.811	1.811	See	See
9	KMeans_short	fit	10000	10000	100	0.708	0.009	20	0.011	0.0	k-means++	0.386	0.007	1.832	1.832	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.007	0.000	random	0.001	0.0	4.066	4.078	See	See
2	KMeans_short	predict	10000	1	2	0.002	0.000	20	0.000	0.002	random	0.000	0.0	13.312	13.713	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.000	20	0.008	0.000	k-means++	0.001	0.0	3.617	3.623	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.001	20	0.000	0.002	k-means++	0.000	0.0	15.272	15.665	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.274	0.000	random	0.001	0.0	2.399	2.410	See	See
8	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	random	0.000	0.0	10.276	10.394	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.000	20	0.260	0.000	k-means++	0.001	0.0	2.651	2.658	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.000	20	0.000	0.002	k-means++	0.000	0.0	9.649	9.800	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 2 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.01	1000000	1000	100	0.000436	0.000444	[20]	1.835441	4.358625e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000786	0.001236	0.55	0.554419	1.032939
4	LogisticRegression	predict	0.07	1000	100	10000	0.001962	0.000180	[26]	4.078155	1.961671e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.006310	0.000652	0.28	0.310900	0.312555

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	12.023	0.367	[20]	0.067	0.000	2.129	0.031	5.647	5.647	See	See
3	LogisticRegression	fit	1000	1000	10000	1.020	0.650	[26]	0.078	0.001	0.822	0.036	1.240	1.242	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	1.835	0.0	0.001	0.001	0.554	1.033	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.013	0.0	0.000	0.000	0.382	0.388	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.078	0.0	0.006	0.001	0.311	0.313	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	0.894	0.0	0.002	0.000	0.050	0.050	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.039624	1000	1000	10000	0.010489	0.000301	NaN	7.627313	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.017926	0.000491	0.122191	0.585093	0.585312

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.202	0.006	0.396	0.0	0.210	0.001	0.964	0.964	See	See
3	Ridge	fit	1000000	1000000	100	1.241	0.076	0.644	0.0	0.366	0.290	3.392	4.327	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	7.627	0.0	0.018	0.0	0.585	0.585	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.127	0.0	0.000	0.0	0.588	0.614	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	4.981	0.0	0.000	0.0	0.506	0.684	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.0	0.572	0.602	See	See